粒子群优化cnn-lstm python

粒子群优化算法（Particle Swarm Optimization，PSO）是一种基于群体智能的优化算法，它模拟了鸟群觅食的行为，通过不断地迭代寻找最优解。而CNN-LSTM是一种深度学习模型，结合了卷积神经网络（Convolutional Neural Network，CNN）和长短时记忆网络（Long Short-Term Memory，LSTM）的优点，可以用于处理序列数据。在Python中，可以使用Keras等深度学习框架来实现CNN-LSTM模型，并结合PSO算法进行模型参数的优化。

以下是实现粒子群优化CNN-LSTM模型的步骤：

# 1. 导入相关库和数据集
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, LSTM
from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from pyswarm import pso

X = np.load('X.npy')
y = np.load('y.npy')

# 2. 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. 定义CNN-LSTM模型
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(100))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# 4. 定义PSO优化函数
def pso_objective_function(params):
    lr, batch_size, epochs = params
    adam = Adam(lr=lr)
    model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
    model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
    y_pred = model.predict_classes(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    return -accuracy

# 5. 运行PSO算法进行模型参数优化
lb = [0.0001, 16, 10]
ub = [0.01, 128, 50]
xopt, fopt = pso(pso_objective_function, lb, ub, swarmsize=10, maxiter=20)

# 6. 使用优化后的参数重新训练模型并进行预测
lr, batch_size, epochs = xopt
adam = Adam(lr=lr)
model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
y_pred = model.predict_classes(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)